# When does difference in sample size become an issue?

• 80past2
In summary, the speaker was conducting a comparison between two groups with sample sizes of 1600 and 700. They found significant differences, but wondered if it was due to the sample size. They tried equalizing the sample sizes and still found similar results. They also performed bootstrapping with similar results. They are now considering using a plotting program to check for normality in the data.
80past2
I was comparing two different groups, and in one, my n was 1600, and the other n was around 700. I found pretty much all significant differences, but is that maybe due to sample size. I tried doing a random selection making the sample sizes equal (both around 700) and got more or less the same numbers and significance every time. Should I do anything else, or is this fine?
I also bootstrapped within one of these restricted sets and got about the same numbers.

Do you need help checking distribution type by plotting it?

80past2 said:
I was comparing two different groups, and in one, my n was 1600, and the other n was around 700. I found pretty much all significant differences, but is that maybe due to sample size. I tried doing a random selection making the sample sizes equal (both around 700) and got more or less the same numbers and significance every time. Should I do anything else, or is this fine?
I also bootstrapped within one of these restricted sets and got about the same numbers.

I am doing a plotting program to look at data-sets and check for normal-ness,
if you'd like, & your data is "near" normal -- you can attach a text file with the data (or a scaled version of it...to obscure what it is) that just lists the data values. eg:
7
3.5
11.0

etc;
And I could plot the data into 1% or 0.05% quantiles; like this:
converting binomial/normal distribution into quantiles and comparing against normal
and then I could post the graphs for you...
It will show skewness, and some information that could help identify what type of distribution it really is, but it's mostly to check Gaussian data...

Last edited:

## 1. When does difference in sample size become an issue?

The difference in sample size becomes an issue when it significantly affects the statistical power and accuracy of the study results. In general, a larger sample size is preferred as it increases the likelihood of detecting a true effect and reduces the margin of error.

## 2. How does a difference in sample size affect statistical power?

A larger sample size increases statistical power, which is the probability of correctly rejecting a null hypothesis. This means that a study with a larger sample size has a better chance of detecting a true effect, whereas a smaller sample size may not have enough power to detect a significant difference.

## 3. What are the consequences of having a small sample size?

A small sample size can lead to inaccurate or unreliable results, as it may not be representative of the entire population being studied. This can also result in a larger margin of error and reduced statistical power, making it difficult to draw meaningful conclusions from the data.

## 4. Can a small sample size be compensated by using advanced statistical methods?

No, a small sample size cannot be compensated by using advanced statistical methods. These methods may help to analyze the data more efficiently, but they cannot overcome the limitations of a small sample size. It is important to have an adequate sample size to ensure the validity and reliability of the study results.

## 5. Is there a specific number for what constitutes a large or small sample size?

The specific number for what constitutes a large or small sample size depends on the study design, research question, and statistical analysis being used. In general, a sample size of at least 30 is considered the minimum for statistical significance, but a larger sample size is preferred for more accurate and reliable results.

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